{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "877d699f-47e2-4399-a384-bd19cdd4440a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data\\MNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\train-images-idx3-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\train-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "import time\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "color_list = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "label_size = 18 # Label size\n",
    "ticklabel_size = 14 # Tick label size\n",
    "\n",
    "# Load the MNIST dataset to display\n",
    "imgDisp = torchvision.datasets.MNIST(root='./data', train=False, download=True)\n",
    "img, label = imgDisp[0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "ax.imshow(img, cmap='gray')\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "ax.set_title(f\"Label: {label}\", fontsize=label_size)\n",
    "# plt.savefig(f'exp_character{label}.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aeafe39d-47b0-4196-a858-ad6d9a2d967c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0: Trainset - 5923 (9.87%), Testset - 980 (9.80%)\n",
      "Class 1: Trainset - 6742 (11.24%), Testset - 1135 (11.35%)\n",
      "Class 2: Trainset - 5958 (9.93%), Testset - 1032 (10.32%)\n",
      "Class 3: Trainset - 6131 (10.22%), Testset - 1010 (10.10%)\n",
      "Class 4: Trainset - 5842 (9.74%), Testset - 982 (9.82%)\n",
      "Class 5: Trainset - 5421 (9.04%), Testset - 892 (8.92%)\n",
      "Class 6: Trainset - 5918 (9.86%), Testset - 958 (9.58%)\n",
      "Class 7: Trainset - 6265 (10.44%), Testset - 1028 (10.28%)\n",
      "Class 8: Trainset - 5851 (9.75%), Testset - 974 (9.74%)\n",
      "Class 9: Trainset - 5949 (9.92%), Testset - 1009 (10.09%)\n",
      "Input_size is 112\n"
     ]
    }
   ],
   "source": [
    "class ftrExtract(object):\n",
    "    def __call__(self, tensor):\n",
    "        tensor = tensor.squeeze()\n",
    "\n",
    "        mean_width = tensor.mean(axis=0)\n",
    "        mean_height = tensor.mean(axis=1)\n",
    "\n",
    "        std_width = tensor.std(axis=0)\n",
    "        std_height = tensor.std(axis=1)\n",
    "\n",
    "        ftrs = torch.cat([mean_width, mean_height, std_width, std_height])\n",
    "\n",
    "        return ftrs\n",
    "\n",
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(), ftrExtract()])\n",
    "\n",
    "# Load the MNIST dataset\n",
    "trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
    "\n",
    "# Count number of each class in trainset\n",
    "train_class_counts = {}\n",
    "for _, label in trainset:\n",
    "    if label not in train_class_counts:\n",
    "        train_class_counts[label] = 0\n",
    "    train_class_counts[label] += 1\n",
    "\n",
    "# Count number of each class in testset\n",
    "test_class_counts = {}\n",
    "for _, label in testset:\n",
    "    if label not in test_class_counts:\n",
    "        test_class_counts[label] = 0\n",
    "    test_class_counts[label] += 1\n",
    "\n",
    "# Print results\n",
    "for i in range(10):\n",
    "    cls_counts_train = train_class_counts.get(i, 0)\n",
    "    cls_ratio_train = cls_counts_train / len(trainset)\n",
    "    cls_counts_test = test_class_counts.get(i, 0)\n",
    "    cls_ratio_test = cls_counts_test / len(testset)\n",
    "\n",
    "    print(f\"Class {i}: Trainset - {cls_counts_train} ({cls_ratio_train:.2%}), Testset - {cls_counts_test} ({cls_ratio_test:.2%})\")\n",
    "\n",
    "batch_size = 42\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0)\n",
    "\n",
    "# Get a batch of training data\n",
    "dataiter = iter(trainloader)\n",
    "data, labels = next(dataiter)\n",
    "\n",
    "input_size = data[0].numpy().shape[0]\n",
    "print(f'Input_size is {input_size}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9cdd1a8-6b1f-4906-9fca-f4e784925200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 加载数据集\n",
    "digits = load_digits()\n",
    "\n",
    "# 选择一个类别进行二分类，例如，将数字'0'和'1'作为两个类别\n",
    "X = digits.data\n",
    "y = (digits.target == 0).astype(int)  # 0类，或者 (digits.target == 1).astype(int) # 1类\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 特征缩放\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 创建线性回归模型\n",
    "model = LogisticRegression()\n",
    "\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 预测测试集\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 打印准确率\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61ef8eb0-bc73-471d-9eba-84c150011e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features and labels from trainset\n",
    "x_train = []\n",
    "y_train = []\n",
    "for image, label in trainset:\n",
    "    x_train.append(image.numpy())\n",
    "    y_train.append(1 if label == 1 else 0)  # Set label to 1 for character 1, 0 otherwise\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "# Extract features and labels from trainset\n",
    "x_test = []\n",
    "y_test = []\n",
    "for image, label in testset:\n",
    "    x_test.append(image.numpy())\n",
    "    y_test.append(1 if label == 1 else 0)  # Set label to 1 for character 1, 0 otherwise\n",
    "\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55c91965-5c11-4288-9a2d-37bcbcd8cd69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 0.31 seconds\n"
     ]
    }
   ],
   "source": [
    "# Define logic regression model\n",
    "mdl_logic = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_logic.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2172b912-e8e1-4af0-b8aa-7a1af5f48d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9665, Recall: 0.9410, Accuracy: 0.9896, F1-Score: 0.9536\n"
     ]
    }
   ],
   "source": [
    "y_pred_logic = mdl_logic.predict(x_test)\n",
    "y_proba_logic = mdl_logic.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_logic)\n",
    "precision = precision_score(y_test, y_pred_logic)\n",
    "recall = recall_score(y_test, y_pred_logic)\n",
    "f1 = f1_score(y_test, y_pred_logic)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e425d696-a0e1-4fa4-8611-79b8e4438096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: imgDisp label is 1, x label is 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 2: imgDisp label is 6, x label is 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 3: imgDisp label is 2, x label is 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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0yGr/qv+ubf+q/8eFe34QeYQoNLiDBw9q1KhRqqio0PXXX69p06apX79+atmyZaDNhx9+WOs7c8nuHZx/2oOWLVtGPBzVxaRJk/T73/9ekvTkk0/qgQceaJDjJiUlWX28I6nGj/KqTjmxdu3aGkdOzqQ+37k3hVGBxtRH/yhXUVGRjhw5UuPHVv5QW3VULJz6ZwrFNdXv3LmzNmzYcFb7Hzp0qNb9jxw5ouLi4mr7JyQkKCEhQaWlpbXuX3VbuOcHkcc1UWhw7777rnw+nxITE/WXv/xF6enpQQFK+r+RgdrU9lGf9H//OVUdGenYsaOkU9eH1OWeV5H00EMP6cknn5QkzZs3r8brgJqKqqMnW7duDXt//+NY18c9HJ06dZIk6+Boq+pzs7bfy78tJiZGiYmJ9d6vuqrrN8v833Kr7Rt8tdX//vvvdeDAgZBtTp48qa+//jpkff/+X331VY3zxlWtXdP+Vb+FeLravqHo/9l2fzR+hCg0uD179kiSevToUeM71w8++KBOdbZv3x5yW2lpqf7xj39IOnUNjd9VV10VeCe/dOnSsPrdkB588EH97ne/k3QqQD300EMR7tHZS0xMVK9evSTZnXv/47h+/foaL8DNz88/Y8gK5aqrrpIkvf/++yovL6/zflUv5Da1TBBakyuuuCJQ48MPP6yxnf/10Lt375DXQ0VK//79A2+AVq5cGbLNrl279NVXX0mSBg0aFFb9jIyMwL9rqr9mzZrA8+H0+v79S0tLA9e9na5q3arHq/rzV199pd27d9e6f8uWLdW/f/+Q+3/yySc1jnz7909OTlaPHj1CtkHjRYhCg/N6vZKkb775JuQfrE2bNumll16qU63Zs2eHXP/73/9eR48eVUxMTOCbTdKpd/7+bys9+eST+uabb2qtH4kLcR988MHAR3i/+93vmkWA8vvFL34h6VRgOFOQOv3c33bbbYqJidHRo0cD5+d0tV2sXptRo0YpOjpaBw8e1PTp0+u8X9UL3P0f6YSjTZs2gRngn3zyyZB/aDdv3hz4humwYcPCPkZ9io+P12233SZJ+s///E+VlJRUazN37lxJpz7eyszMDKv+RRddFAgmv//970Ne0/XEE09IOhVCrrnmmqBt6enpgevc/O2qOn78eOC51L9/f1100UVB22+99VYlJCTIGBNy/+LiYi1cuFDSqednfHx80Pbhw4crOjpapaWlgesaqyooKAi8DkaMGBHiDKDRi+T8Cmj6qs6LdODAgVoX/321vvnmm8CcPEOGDAnMpFxRUWFefvllc/7555vzzjuvxvl3/Mf0z3L9q1/9yhw4cMAYc2pulscffzxQ//7776+2//bt2wP1zz//fPP888+b4uLiwPYDBw6Y1157zdx6661m0KBB1fb396s+5onyz8AuycyfPz/s/avOGxRqVu4zqcuM5Wc6bm3Ky8sDs5XHxMSY3/zmN0H3ACwrKzOrVq0y9913n2nTpk21/f0zikdFRZnf/va3gbmmvv/++8DcYv7nRbgzlvtnt9b/n5/sm2++CWz7/vvvzdKlS01mZmbQPmVlZSY2NtZIMvPmzTOVlZUhf+/a5onasGFDYIb3/v37my1bthhjjDl58qR55513TOfOnY0kc/HFFwdufutX1/Ne23P2bOaJMib4BsQDBgwInLfDhw+bmTNnBmaTr+kGxGc6ftUbEFf9/+LgwYOBm/tKdbsB8b333msOHjxojDHmu+++C8xDVZcbELtcLjNz5kxz+PBhY8ypucX89/Wryw2IY2NjzbPPPhuYiX/9+vWmV69eRjp138Gq/weh6SBE4aycfvuW2paqkzM+/PDDQdu8Xm/gD8mFF15o/vSnP50xRFW97UtUVJRp27Zt4D9bSWbgwIHm6NGjIfu9YcMGk5KSEmjrv22M/7YZVWucrr5C1K5duwL7RkVFVbuNyOnL6RMvGtP4Q5Qxp0Lq9ddfH3SePR5P0O1b/CHrdEePHjUDBw4MtImOjjaJiYmO3fbFH8T8S+vWrWu87Yvf2LFjA9tbtWplunbtapKTk4MmXjzTbV+WLl0aCGP+8xEXFxf4uS63falNfYYoY4x55513qp2nqq/FUaNG1Rgw63L85557Lug2Nqc/V850s+mqr0uXyxWYHNP/PHvuuedq3LeystKMHj066DlX9TZFrVq1Mu+8806N+59+q6IWLVoE3WLqvPPOM+vXr6+1/2i8+DgPEfHEE0/of/7nfwLfyjt+/Li6deumKVOmaOPGjXX+lsrcuXO1dOlSXX311aqsrFRsbKzS0tL09NNPa+XKlTXO+XP55Zfryy+/1IIFCzRw4EC1a9cuME9V9+7dddddd2np0qVBt/Wob1XnyKqsrNT+/ftrXULdAsR/UXVUVJT++Z//ucH6Ho527drpgw8+0Jtvvqnbb79dSUlJqqio0NGjR3XBBRfopptu0oIFC0Je5B0XF6cVK1bo6aefVlpammJjY2WM0YABA/TKK6+E/MilrqKjo7VgwQLl5eXp7rvvVteuXXX8+HHFxsbqsssu09ixY4MmbvX7j//4D82YMSNwEfLu3bu1a9cu/fDDD3U+9tChQ/XFF19o/Pjxuvjii1VRUaGYmBilpaVp5syZ+vzzz3XppZda/2717eabb9aWLVs0btw4paSk6OjRo2rTpo0yMjK0bNkyZWdnn9W3CrOysrRu3TrddddduuCCC3TkyBG1b99emZmZ+vDDD884yeqMGTP04YcfKjMzU+3bt9eRI0d0wQUXBG52npWVVeO+LpdLL7zwgpYtW6aMjAwlJiaqvLxcycnJGjdunDZv3qybb765xv3dbrdWrFih5557Tv3791d8fLyOHz+u7t27a8KECfriiy9qnTIEjZvLGIurIQE0SllZWXr++ec1fPhw7goPAPWMEAU0IxdddJG+++47ff3119UukgUAOIuP84BmYteuXdq5c6fGjh1LgAKABsBIFAAAgAVGogAAACwQogAAACwQogAAACzERLoD4aisrNTevXuVkJDQqO5kDgAAmgdjjEpLS9W5c+eg+2OG0qRC1N69e5WUlBTpbgAAgGZuz5496tKlS61tmtTHeQkJCZHuAgAAOAfUJXM0qRDFR3gAAKAh1CVzNFiI+uyzz3TzzTcrMTFR8fHx6tevn1566aWGOjwAAICjGuSaqNzcXA0ePFixsbG688475fV6tXz5ct19990qKCjQlClTGqIbAAAAjqn3GctPnDihnj176rvvvtPf//53XX755ZKk0tJS/cu//Iu2bdumL7/8Ut27dz9jLZ/PJ6/XW5/dBQAAUElJiTweT61t6v3jvI8++kjbt2/XXXfdFQhQ0qkLth599FGdOHFC2dnZ9d0NAAAAR9V7iMrNzZUkDRo0qNo2/7qPP/64vrsBAADgqHoPUfn5+ZIU8uO6xMREtWvXLtAGAACgqaj3C8tLSkokqcZrmTwej7777ruQ2yoqKlRRURH42efzOd9BAAAAC416nqg5c+bI6/UGFmYrBwAAjUW9hyj/CJR/ROp0tX3jbvLkySopKQkse/bsqbd+AgAAhKPeQ5T/WqhQ1z0VFRXphx9+qHF6A7fbLY/HE7QAAAA0BvUeotLT0yVJ7733XrVt/nX+NgAAAE1Fg0y22aNHDxUWFmrt2rVKS0uTFDzZ5hdffKFLLrnkjLWYbBMAADSEuky2We/fzouJidGiRYs0ePBgDRgwQMOGDZPH49Hy5cu1c+dOPfbYY3UKUAAAAI1JvY9E+X366aeaPn26/v73v+vYsWO67LLL9MADD+juu++ucw1GogAAQEOoy0hUg4UoJxCiAABAQ2gU984DAABojghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFmIi3QEAqE9RUc6+V2zRooWj9RqjY8eOOVrPGONoPaCxYCQKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAQkykOwCgeejZs6djtUaPHu1YrYsvvtixWpI0ZMgQR+s1Rv/1X//laL1Vq1Y5VuvNN990rFZFRYVjtXBuYiQKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAgssYYyLdibry+Xzyer2R7gaAEN5//33Hat1www2O1WrMjh075litmJgYx2pFRTXe99cbN250rJaTz7Pi4mLHaqFxKCkpkcfjqbVN432lAAAANGKEKAAAAAuEKAAAAAsNEqJSUlLkcrlCLvfcc09DdAEAAMBRzl2JeAZer1cPPPBAtfV9+/ZtqC4AAAA4psFCVJs2bTRjxoyGOhwAAEC94pooAAAACw02ElVRUaHFixersLBQiYmJuuqqq9S7d++GOjwAAICjGixE7du3T6NGjQpad+ONN+rFF19Uu3btQu5TUVGhioqKwM8+n68+uwgAAFBnDfJx3pgxY5Sbm6sDBw7I5/Np7dq1uummm7Ry5UrdcsstqmnS9Dlz5sjr9QaWpKSkhuguAADAGUXsti+VlZVKT09XXl6e3n77bf34xz+u1ibUSBRBCmicuO1L+LjtS/i47QsaSqO+7UtUVJRGjx4tSVqzZk3INm63Wx6PJ2gBAABoDCL6dsN/LdSRI0ci2Q0AAICwRTRErVu3TtKpGc0BAACaknoPUV9++WXIz4rz8vI0f/58ud1uDRkypL67AQAA4Kh6n+LglVde0bx583TDDTcoJSVFbrdbn3/+ud577z1FRUVp4cKF6tq1a313AwAAwFH1HqKuu+46ffXVV9qwYYM+/vhjlZeXq0OHDho6dKgmTJigfv361XcXAAAAHFfvISo9PV3p6en1fRgAAIAG1WAzlgNo3t58803HavXq1cuxWi+88IJjtSRp7969jtX69ttvHavVvn17x2rdcccdjtWSpFtuucWxWpdffrljtR555JFGWQtNR+OdUQ0AAKARI0QBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYiIl0BwA0DwsWLGiUtRC+1NRUR+v967/+q2O1oqKce+/v8Xgcq+VyuRyrJUnGGEfroX4wEgUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGDBZYwxke5EXfl8Pnm93kh3AwAQhttvv92xWtnZ2Y7Vio+Pd6zWn/70J8dqSdKIESMcrYfwlZSUyOPx1NqGkSgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALhCgAAAALMZHuAACgeSstLXWsVmxsrGO1nNSyZctIdwERwEgUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACAhZhIdwAA0Lx5vV7HakVFNc73/ocOHYp0FxABjfPZCAAA0MgRogAAACwQogAAACyEHaKWLFmi8ePHq2/fvnK73XK5XMrJyamxvc/n08SJE5WcnCy3263k5GRNnDhRPp/vbPoNAAAQUWFfWD516lTt2rVL7dq1U6dOnbRr164a25aVlSk9PV2bNm1SRkaGhg0bps2bN+upp57SqlWrlJeXp/j4+LP6BQAAACIh7JGoRYsWqaCgQAcOHNA999xTa9t58+Zp06ZNmjRpkt577z098cQTWrFihaZNm6ZNmzZp3rx51h0HAACIpLBD1MCBA5WcnHzGdsYYLVq0SK1bt9a0adOCtk2ePFmJiYl6/vnnZYwJtwsAAAARV28Xlufn52vv3r26+uqrq31kFxcXp2uuuUaFhYX69ttv66sLAAAA9aZeQ5Qkde/ePeR2/3p/u1AqKirk8/mCFgAAgMag3kJUSUmJpJpnqvV4PEHtQpkzZ468Xm9gSUpKcr6jAAAAFhr1PFGTJ09WSUlJYNmzZ0+kuwQAACCpHu+d5x+Bqmmkyf/RXG33VHK73XK73c53DgAA4CzV20jUma55OtM1UwAAAI1ZvYaozp07a82aNSorKwvaVl5ertWrV6tz587q1q1bfXUBAACg3tRbiHK5XMrKytLhw4c1a9asoG1z5sxRUVGRsrKy5HK56qsLAAAA9Sbsa6IWLVqkvLw8SdLWrVsD63JzcyVJmZmZyszMlCRNmjRJb731lubNm6eNGzeqT58+2rx5s1asWKG0tDRNmjTJmd8CAACggYUdovLy8rR48eKgdWvWrNGaNWskSSkpKYEQFR8fr9zcXM2cOVPLli1Tbm6uOnbsqAkTJmj69OncNw8AADRZYYeonJwc5eTk1Lm91+vV/PnzNX/+/HAPBQAA0Gg16nmiAAAAGqt6mycKAABJ2rFjh2O1jhw54lithIQEx2rddNNNjtVC08FIFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgAVCFAAAgIWYSHcAANC43HTTTY7We/311x2rFRsb61gtJ82cOTPSXUAEMBIFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABgISbSHUDT0qNHD8dqjR8/3rFaktSnTx9H6zVGr776qqP1tm3b5litDRs2OFbr4MGDjtVC+C644AJH68XGxjparzHauXNnpLuACGAkCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhCgAAwEJMpDuA0Hr16uVYreeff96xWikpKY7V6tChg2O1zhUDBgyIdBdq9PXXXztWq6SkxLFa2dnZjtWSpJdfftmxWk7+nl26dHGsVmpqqmO1GrMZM2Y4VmvVqlWO1ULTwUgUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABUIUAACABZcxxkS6E3Xl8/nk9Xoj3Y0GMXv2bMdq/eY3v3GslpM2b97saL0dO3Y4Wq8x+tGPfuRovU6dOjla71ywbds2x2odPXrUsVpt27Z1rFbXrl0dq+W07du3O1YrNTXVsVrl5eWO1ULjUFJSIo/HU2sbRqIAAAAsEKIAAAAshB2ilixZovHjx6tv375yu91yuVzKyckJ2XbGjBlyuVwhl7i4uLPtOwAAQMTEhLvD1KlTtWvXLrVr106dOnXSrl27zrjPyJEjlZKSEnzgmLAPDQAA0GiEnWQWLVqk7t27Kzk5WU888YQmT558xn1GjRqla6+91qZ/AAAAjVLYIWrgwIH10Q8AAIAmpUE+U/vkk0/06aefKjo6Wj179tTAgQPldrsb4tAAAAD1okFC1LRp04J+7tSpkxYvXqyMjIyGODwAAIDj6nWKg7S0NC1evFgFBQU6evSo8vPzNXv2bBUXF+uWW24542SLFRUV8vl8QQsAAEBjUK8hKjMzUz//+c+VnJysuLg4devWTVOnTtXTTz+t8vJyPfbYY7XuP2fOHHm93sCSlJRUn90FAACos4hMtjly5EjFxMRozZo1tbabPHmySkpKAsuePXsaqIcAAAC1i8hkTbGxsUpISNCRI0dqbed2u7kAHQAANEoRGYnKz89XUVFRtQk4AQAAmop6C1GlpaXasmVLtfVFRUUaO3asJGnYsGH1dXgAAIB6ZTVjeV5eniRp69atgXW5ubmSTl1MnpmZqYMHD6p3797q27evUlNT1b59exUWFmrFihU6ePCgMjIyNGHCBOd+EwAAgAYUdojKy8vT4sWLg9atWbMmcJF4SkqKMjMz1bZtW913331au3at/vKXv6i4uFjx8fFKTU3V8OHDlZWVpejoaGd+CwAAgAYWdojKyclRTk7OGdt5PB4tWLDApk8AAACNXkQuLAcAAGjqIjLFAZquY8eOOVZr0qRJjtWSpPfff9/Reo1R165dHa136aWXOlbroYcecqzW9ddf71gtp/Xo0SPSXTinzZ0717Fa5eXljtXCuYmRKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAsxke4AQrvvvvsi3YWQ3n33Xcdqvf/++47VOlfs3r270dZzu92O1erWrZtjtbp27epYLdhZv369Y7X+9Kc/OVYLOFuMRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFhwGWNMpDtRVz6fT16vN9LdaBAbN250rFbv3r0dq1VZWelYreeee86xWpL0j3/8w9F654IuXbo4Vuvhhx92rJbb7XasFpqX1157zbFaTz75pGO1Pv30U8dqoXEoKSmRx+OptQ0jUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABZcxhgT6U7Ulc/nk9frjXQ3GsSgQYMcq7Vy5UrHagHnup07dzpW691333WslpNGjhzpaL2EhATHajn5J2vo0KGO1Xr11Vcdq4XGoaSkRB6Pp9Y2jEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYcBljTKQ7UVc+n09erzfS3WgQCQkJjtX67W9/61it++67z7FaaF4KCwsdq/Xqq686VuuFF15wrJYkFRQUOFbr8OHDjtVyUps2bRytt3jxYsdq/eQnP3GslsfjcaxWY30sYa+kpOSMzxFGogAAACwQogAAACyEFaIKCwv1hz/8QYMGDVLXrl0VGxurjh076rbbbtO6detC7uPz+TRx4kQlJyfL7XYrOTlZEydOlM/nc+QXAAAAiISwQtQzzzyjCRMmaMeOHcrIyNCvf/1r9e/fX2+++aauuuoqvfLKK0Hty8rKlJ6erqeeeko9evTQhAkT1KtXLz311FNKT09XWVmZo78MAABAQ4kJp3G/fv20evVqDRgwIGj9J598ohtuuEH33nuvfvrTn8rtdkuS5s2bp02bNmnSpEmaO3duoP306dM1a9YszZs3TzNnznTg1wAAAGhYYY1EDRkypFqAkqQBAwbouuuu06FDh7R161ZJkjFGixYtUuvWrTVt2rSg9pMnT1ZiYqKef/55NaEvBwIAAAQ4dmF5ixYtJEkxMacGt/Lz87V3715dffXVio+PD2obFxena665RoWFhfr222+d6gIAAECDcSRE7d69Wx988IE6duyo1NRUSadClCR179495D7+9f52AAAATUlY10SFcvz4cY0YMUIVFRWaN2+eoqOjJZ2apEpSjZNj+iew8rcLpaKiQhUVFYGf+UYfAABoLM5qJKqyslJjxozR6tWrNW7cOI0YMcKpfkmS5syZI6/XG1iSkpIcrQ8AAGDLOkQZYzRu3DgtWbJEw4cP18KFC4O2+0egahpp8o8q1XYbl8mTJ6ukpCSw7Nmzx7a7AAAAjrL6OK+yslJZWVnKzs7WsGHDlJOTo6io4Dx2pmueznTNlCS53e7AdAkAAACNSdgjUVUD1NChQ/Xiiy8GroOqqnv37urcubPWrFlTbVLN8vJyrV69Wp07d1a3bt3sew8AABAhYYWoyspKjR07VtnZ2brjjju0ZMmSkAFKklwul7KysnT48GHNmjUraNucOXNUVFSkrKwsuVwu+94DAABESFgf582aNUs5OTlq3bq1LrnkEj322GPV2mRmZiotLU2SNGnSJL311luaN2+eNm7cqD59+mjz5s1asWKF0tLSNGnSJEd+CQAAgIYWVogqKCiQJB0+fFiPP/54yDYpKSmBEBUfH6/c3FzNnDlTy5YtU25urjp27KgJEyZo+vTp1SbhBAAAaCrCClE5OTnKyckJ6wBer1fz58/X/Pnzw9oPAACgMXPsti8AAADnEpdpQncA9vl8tc4rhdCcvHjfyY9gQ93M+mxs2bLFsVq//OUvHavVmBUWFjpWa9GiRY7VOnr0qGO1EHn+e6s6wclpb07/5vjZaEJ/SlFHJSUlgbur1ISRKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAuEKAAAAAsuY4yJdCfqyufzyev1RrobAACgmSspKZHH46m1DSNRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFghRAAAAFsIKUYWFhfrDH/6gQYMGqWvXroqNjVXHjh112223ad26ddXaz5gxQy6XK+QSFxfn2C8BAADQ0GLCafzMM89o7ty5uvjii5WRkaH27dsrPz9fb7zxht544w39+c9/1s9+9rNq+40cOVIpKSnBB44J69AAAACNSlhJpl+/flq9erUGDBgQtP6TTz7RDTfcoHvvvVc//elP5Xa7g7aPGjVK11577Vl3FgAAoLEI6+O8IUOGVAtQkjRgwABdd911OnTokLZu3epY5wAAABorxz5Ta9GixamCIT6m++STT/Tpp58qOjpaPXv21MCBA6uNVgEAADQljoSo3bt364MPPlDHjh2Vmppabfu0adOCfu7UqZMWL16sjIyMWutWVFSooqIi8LPP53OiuwAAAGftrKc4OH78uEaMGKGKigrNmzdP0dHRgW1paWlavHixCgoKdPToUeXn52v27NkqLi7WLbfcos2bN9dae86cOfJ6vYElKSnpbLsLAADgDHMWTp48aYYPH24kmXHjxtV5v//+7/82ksztt99ea7vy8nJTUlISWPbs2WMksbCwsLCwsLDU61JSUnLGPGMdoiorK82YMWOMJDN8+HBz8uTJOu9bUVFhYmJiTKdOncI6ZklJScRPKgsLCwsLC0vzX+oSoqw+zqusrNTYsWP1wgsvaNiwYcrJyVFUVN1LxcbGKiEhQUeOHLE5PAAAQMSFHaIqKyuVlZWl7OxsDR06VC+++GLQdVB1kZ+fr6KiomoTcAIAADQVYYUo/whUdna27rjjDi1ZsqTGAFVaWqotW7ZUW19UVKSxY8dKkoYNG2bRZQAAgMgLa4qDWbNmKScnR61bt9Yll1yixx57rFqbzMxMpaWl6eDBg+rdu7f69u2r1NRUtW/fXoWFhVqxYoUOHjyojIwMTZgwwbFfBAAAoCGFFaIKCgokSYcPH9bjjz8esk1KSorS0tLUtm1b3XfffVq7dq3+8pe/qLi4WPHx8UpNTdXw4cOVlZUV9seAAAAAjYXLGGMi3Ym68vl88nq9ke4GAABo5kpKSuTxeGptc9aTbQIAAJyLCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWCFEAAAAWmlSIMsZEugsAAOAcUJfM0aRCVGlpaaS7AAAAzgF1yRwu04SGdyorK7V3714lJCTI5XLV2M7n8ykpKUl79uyRx+NpwB5C4vw3BjwGkcX5jyzOf2Q19fNvjFFpaak6d+6sqKjax5piGqhPjoiKilKXLl3q3N7j8TTJB7C54PxHHo9BZHH+I4vzH1lN+fx7vd46tWtSH+cBAAA0FoQoAAAAC80yRLndbk2fPl1utzvSXTkncf4jj8cgsjj/kcX5j6xz6fw3qQvLAQAAGotmORIFAABQ3whRAAAAFghRAAAAFghRAAAAFppViPrss8908803KzExUfHx8erXr59eeumlSHfrnJGSkiKXyxVyueeeeyLdvWZhyZIlGj9+vPr27Su32y2Xy6WcnJwa2/t8Pk2cOFHJyclyu91KTk7WxIkT5fP5Gq7TzUw4j8GMGTNqfE3ExcU1bMebgcLCQv3hD3/QoEGD1LVrV8XGxqpjx4667bbbtG7dupD78BpwTrjn/1x4/jepGctrk5ubq8GDBys2NlZ33nmnvF6vli9frrvvvlsFBQWaMmVKpLt4TvB6vXrggQeqre/bt2/Dd6YZmjp1qnbt2qV27dqpU6dO2rVrV41ty8rKlJ6erk2bNikjI0PDhg3T5s2b9dRTT2nVqlXKy8tTfHx8A/a+eQjnMfAbOXKkUlJSgtbFxDSb/34bzDPPPKO5c+fq4osvVkZGhtq3b6/8/Hy98cYbeuONN/TnP/9ZP/vZzwLteQ04K9zz79esn/+mGTh+/Li5+OKLjdvtNhs2bAis9/l85rLLLjMxMTHmm2++iWAPzw3JyckmOTk50t1o1t5//31TUFBgjDFmzpw5RpLJzs4O2XbatGlGkpk0aVLI9dOmTavv7jZL4TwG06dPN5LMqlWrGq6Dzdhrr71mVq9eXW396tWrTYsWLUzbtm1NeXl5YD2vAWeFe/7Phed/s/g476OPPtL27dt111136fLLLw+sT0hI0KOPPqoTJ04oOzs7gj0EnDFw4EAlJyefsZ0xRosWLVLr1q01bdq0oG2TJ09WYmKinn/+eRmmiQtbXR8DOG/IkCEaMGBAtfUDBgzQddddp0OHDmnr1q2SeA3Uh3DO/7miWYyn5ebmSpIGDRpUbZt/3ccff9yQXTpnVVRUaPHixSosLFRiYqKuuuoq9e7dO9LdOufk5+dr7969Gjx4cLWPK+Li4nTNNdfozTff1Lfffqvu3btHqJfnjk8++USffvqpoqOj1bNnTw0cOPCcmM25IbVo0ULS/31MxGugYZ1+/qtqzs//ZhGi8vPzJSnkCyExMVHt2rULtEH92rdvn0aNGhW07sYbb9SLL76odu3aRaZT56DaXhNV1+fn5/MHpAGcPhLSqVMnLV68WBkZGRHqUfOye/duffDBB+rYsaNSU1Ml8RpoSKHOf1XN+fnfLD7OKykpkXTqouZQPB5PoA3qz5gxY5Sbm6sDBw7I5/Np7dq1uummm7Ry5UrdcsstDJs3oLq8Jqq2Q/1IS0vT4sWLVVBQoKNHjyo/P1+zZ89WcXGxbrnlFm3evDnSXWzyjh8/rhEjRqiiokLz5s1TdHS0JF4DDaWm8y+dG8//ZjEShcbh9HcbV155pd5++22lp6crLy9P7777rn784x9HqHdAw8vMzAz6uVu3bpo6dao6dOigX/ziF3rsscf06quvRqZzzUBlZaXGjBmj1atXa9y4cRoxYkSku3ROOdP5Pxee/81iJMr/TqOmdxQ+n6/GdyOoX1FRURo9erQkac2aNRHuzbmjLq+Jqu3QsEaOHKmYmBheE2fBGKNx48ZpyZIlGj58uBYuXBi0nddA/TrT+a9Nc3r+N4sQVfWz7dMVFRXphx9+4DPvCPJfC3XkyJEI9+TcUdtroup6XheRERsbq4SEBF4TliorKzV27Fi98MILGjZsmHJychQVFfznjNdA/anL+a9Nc3r+N4sQlZ6eLkl67733qm3zr/O3QcPzz2R7+mRrqD/du3dX586dtWbNGpWVlQVtKy8v1+rVq9W5c2d169YtQj08t+Xn56uoqIjXhIXKykplZWUpOztbQ4cO1Ysvvhh0HY4fr4H6UdfzX5vm9PxvFiHqhhtu0EUXXaSXXnpJmzZtCqwvLS3V7NmzFRMTU+0bY3DWl19+qeLi4mrr8/LyNH/+fLndbg0ZMqThO3aOcrlcysrK0uHDhzVr1qygbXPmzFFRUZGysrLkcrki1MPmr7S0VFu2bKm2vqioSGPHjpUkDRs2rKG71aT5R0Cys7N1xx13aMmSJTX+Aec14Lxwzv+58vx3mWbylalVq1Zp8ODBcrvdGjZsmDwej5YvX66dO3fqscce029+85tId7FZmzFjhubNm6cbbrhBKSkpcrvd+vzzz/Xee+8pKipKCxcuVFZWVqS72eQtWrRIeXl5kqStW7dqw4YNuvrqqwPvpjMzMwMXc5aVlal///6BW1706dNHmzdv1ooVK5SWlsYtLyzV9TEoKCjQhRdeqL59+yo1NVXt27dXYWGhVqxYoYMHDyojI0Nvv/22YmNjI/nrNCkzZszQzJkz1bp1a91///0h5yTKzMxUWlqaJF4DTgvn/J8zz/+IzZVeD9atW2duvPFG4/V6TcuWLU3fvn3NkiVLIt2tc0Jubq752c9+Zrp162YSEhJMixYtTJcuXcydd95p1q1bF+nuNRsjR440kmpcpk+fHtS+uLjYTJgwwSQlJZkWLVqYpKQkM2HCBFNcXByZX6AZqOtjUFJSYu677z7Tp08f065dOxMTE2O8Xq/p37+/WbhwoTlx4kRkf5Em6EznXiFuwcNrwDnhnP9z5fnfbEaiAAAAGlKzuCYKAACgoRGiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALBCiAAAALPw/uEX6uWygssgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random select 3 examples from imgDisp and testset\n",
    "np.random.seed(42)\n",
    "idx = np.random.choice(len(imgDisp), 3)\n",
    "\n",
    "# Select instances\n",
    "imgDisp_select = [imgDisp[i] for i in idx]\n",
    "x_select = x_test[idx]\n",
    "y_select = y_test[idx]\n",
    "\n",
    "y_select_proba = mdl_logic.predict_proba(x_select)\n",
    "\n",
    "# Check the selected instances' labels are the same\n",
    "for i in range(len(idx)):\n",
    "    print(f'Sample {i+1}: imgDisp label is {imgDisp_select[i][1]}, x label is {y_select[i]}')\n",
    "\n",
    "    # Display image from imgDisp\n",
    "    fig, ax = plt.subplots(figsize=(7,7))\n",
    "    ax.imshow(imgDisp_select[i][0], cmap='gray')\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "    ax.set_title(f\"Label: {imgDisp_select[i][1]}, Prediction: {y_select_proba[i,1]:.4f}\", fontsize=label_size)\n",
    "\n",
    "    # plt.savefig(f'binary_prediction_{i+1}.png', dpi=300) # Make figure clearer\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5388011-7e2d-453a-a110-6f648d6cf9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_recall_curve, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成模拟数据集\n",
    "X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建逻辑回归模型\n",
    "logistic_model = LogisticRegression()\n",
    "\n",
    "# 训练模型\n",
    "logistic_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测概率\n",
    "y_prob = logistic_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# 计算精确率-召回率曲线\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_prob)\n",
    "\n",
    "# 计算ROC曲线\n",
    "fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
    "\n",
    "# 绘制精确率-召回率曲线\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(recall, precision, marker='.')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(fpr, tpr, marker='.')\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c947a06-1f40-4423-8838-2970b3946414",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\envs\\machinelearning\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 加载手写数字数据集\n",
    "digits = load_digits()\n",
    "\n",
    "# 选择一个子集进行多分类，例如，只选择数字0到4\n",
    "X = digits.data[digits.target < 5]\n",
    "y = digits.target[digits.target < 5]\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 特征缩放\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 创建Softmax回归模型\n",
    "softmax_model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=1000)\n",
    "\n",
    "# 训练模型\n",
    "softmax_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测概率\n",
    "y_prob_softmax = softmax_model.predict_proba(X_test)\n",
    "\n",
    "# 计算精确率-召回率曲线和ROC曲线（对于多分类问题，需要微调）\n",
    "# 这里我们只展示如何训练模型和预测，精确率-召回率曲线和ROC曲线的计算需要针对多分类问题进行调整\n",
    "\n",
    "# 绘制精确率-召回率曲线和ROC曲线的代码需要根据多分类问题进行调整，这里省略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8904b4a-81c7-40b7-9d93-2b81093c7263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.metrics import precision_recall_curve, roc_curve, auc, average_precision_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 加载手写数字数据集\n",
    "digits = datasets.load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建SVM一对多模型\n",
    "svm_model = OneVsRestClassifier(SVC(kernel='linear', probability=True, random_state=42))\n",
    "\n",
    "# 训练模型\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# 预测概率\n",
    "y_prob = svm_model.predict_proba(X_test)\n",
    "\n",
    "# 计算每个类别的精确率-召回率曲线和ROC曲线\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "for i in range(len(np.unique(y))):\n",
    "    precision[i], recall[i], _ = precision_recall_curve(y_test == i, y_prob[:, i])\n",
    "    average_precision[i] = average_precision_score(y_test == i, y_prob[:, i])\n",
    "    fpr, tpr, _ = roc_curve(y_test == i, y_prob[:, i])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# 绘制精确率-召回率曲线\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "for i in range(len(np.unique(y))):\n",
    "    plt.plot(recall[i], precision[i], label=f'Class {i} (AP = {average_precision[i]:0.2f})')\n",
    "plt.title('Precision-Recall curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 2)\n",
    "for i in range(len(np.unique(y))):\n",
    "    plt.plot(fpr, tpr, label=f'Class {i} (AUC = {roc_auc:0.2f})')\n",
    "plt.title('ROC curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9fcd0421-35e7-41fd-af63-9416e37111e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features and labels from trainset\n",
    "x_train = []\n",
    "y_train = []\n",
    "for image, label in trainset:\n",
    "    x_train.append(image.numpy())\n",
    "    y_train.append(label)\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "# Extract features and labels from trainset\n",
    "x_test = []\n",
    "y_test = []\n",
    "for image, label in testset:\n",
    "    x_test.append(image.numpy())\n",
    "    y_test.append(label)\n",
    "\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f8e83b0-42a1-475f-9be6-8719614cbe76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 4.80 seconds\n",
      "Accuracy: 0.8535\n"
     ]
    }
   ],
   "source": [
    "# Define logic multi-classifier\n",
    "mdl_logic_ovr = OneVsRestClassifier(LogisticRegression(max_iter=1000))\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_logic_ovr.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Make predictions and evaluate the model\n",
    "y_pred_logic_ovr = mdl_logic_ovr.predict(x_test)\n",
    "y_proba_logic_ovr = mdl_logic_ovr.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_logic_ovr)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a72fa3f0-4b19-4a84-89c5-82d3f463feab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training class 0, Training time: 0.47 seconds\n",
      "Training class 1, Training time: 0.34 seconds\n",
      "Training class 2, Training time: 0.53 seconds\n",
      "Training class 3, Training time: 0.56 seconds\n",
      "Training class 4, Training time: 0.50 seconds\n",
      "Training class 5, Training time: 0.67 seconds\n",
      "Training class 6, Training time: 0.39 seconds\n",
      "Training class 7, Training time: 0.39 seconds\n",
      "Training class 8, Training time: 0.68 seconds\n",
      "Training class 9, Training time: 0.47 seconds\n"
     ]
    }
   ],
   "source": [
    "# Get class list: 0, 1, ..., 9\n",
    "class_list = np.sort(np.unique(y_train))\n",
    "\n",
    "# Create model list\n",
    "mdl_logic_list = []\n",
    "for c in class_list:\n",
    "    mdl_logic_list.append(LogisticRegression(max_iter=1000))\n",
    "\n",
    "# Train models seperately\n",
    "for i in range(len(class_list)):\n",
    "    start_time = time.time()\n",
    "    mdl_logic_list[i].fit(x_train, (y_train == class_list[i]).astype(int))\n",
    "    end_time = time.time()\n",
    "    print(f'Training class {class_list[i]}, Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b8cd91b7-0b1c-4022-945b-b90c29fed8bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: imgDisp label is 9, testset label is 9, predict label is 9\n",
      "Sample 2: imgDisp label is 2, testset label is 2, predict label is 0\n",
      "Sample 3: imgDisp label is 2, testset label is 2, predict label is 2\n",
      "Sample 4: imgDisp label is 9, testset label is 9, predict label is 8\n",
      "Sample 5: imgDisp label is 8, testset label is 8, predict label is 8\n",
      "Sample 6: imgDisp label is 5, testset label is 5, predict label is 5\n",
      "Sample 7: imgDisp label is 7, testset label is 7, predict label is 7\n",
      "Sample 8: imgDisp label is 7, testset label is 7, predict label is 7\n",
      "Sample 9: imgDisp label is 4, testset label is 4, predict label is 4\n",
      "Sample 10: imgDisp label is 5, testset label is 5, predict label is 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kJSXp22+/rXU6i/o+1oqvscWLF2vQoEH163iItW/fXgcOHKg15B8+fFgHDx4MtkfDwDVRCJt//OMf8vv9SkxM1N///nelp6dXClDS/40M1Ka2jwEkBX9RVRwZCfwiP3LkyFl/j6rx48fr2WeflSRlZ2fXeB1QQ1Fx9KTiCEVdBZ7Huj7v9dGuXTtJCvsNbyu+Nmt7XIFtMTExSkxMDHm/zsTx48eD1x3Onj1b9913X5UAdfLkyeCIUW1qOyeB5zkmJiY4AWzFW/rYvMZCLfDNu8C39KpTcdsVV1wR8j7BGYQohE1gpu1LLrmkxgss33333TrV2bZtW7XbSkpK9J///EeSgrcqkaRrrrkm+NftqReknk0effRR/fa3v5X0Q4AaP358hHt05hITE3X55ZdLsjv3gefxk08+qfGC24KCgtOGrOpcc801kqR//vOfOnr0aJ33q3ghd20jKjW56qqrgjVquyVM4P3QrVu3aq+HOpvs27cveA6vvPLKatvk5+fX6TxX/GJBTdt+/OMfVzongWvyXn311SrXWEZaRkaGJOnLL7/Uzp07q23z1ltvSfphtLxXr15h6xvODCEKYRP4S3Hr1q3V/iLdsGGDFi1aVKdaTz31VLXrf/e73+nIkSOKiYkJfrNJ+uEv/8C3lZ599llt3bq11vrhvBA34NFHHw1+hPfb3/62UQSogF/84heSfggMpwtSp577O+64QzExMTpy5Ejw/JyqtovVazNs2DBFR0dr//79mjx5cp33q3iBe+AjmPpo3rx5cAb4Z599ttqPmDdu3Bj8hmlWVla9jxFuHo8n+IfKxo0bq2w/ceLEaeeHCpgzZ061I1ZfffVVcIb6Uz+yC7zGtm7dGhzJrUlpaamOHTtWp7444fbbb1dCQoKMMXrmmWeqbD948KDmzJkj6YfXe3x8fNj6hjMUudkV0JBVnBdp3759tS7FxcXGGGO2bt0anJNnwIABwblcysrKzCuvvGLOP/9807Jlyxrn3wkcMzDL9YMPPmj27dtnjDHG7/ebp59+Olj/oYceqrL/tm3bgvXPP/98M2/ePHPw4MHg9n379pnXXnvN3H777ebGG2+ssn+gX6GYJyowA7skM2vWrHrvX3HeIJt5feoyY/npjlubo0ePBmcrj4mJMb/+9a/Nzp07g9tLS0vNqlWrzAMPPGCaN29eZf/AjOJRUVHmN7/5TXCuqe+++y44t1jgdVHfGct/9atfBR/DiBEjzNatW4PbvvvuO7N48WKTmZlZaZ/S0tJK8xWVl5dX+7hrmydq/fr1wRnee/XqZTZt2mSMMebkyZPmzTffNO3btzeSzEUXXWRKSkoq7VvX817ba7Y2FedFeuGFF077Hg/MD9arVy8jyXTo0MG89957wfmrNm/ebDIyMozb7Q7O03Tqc1HxmF6v16SmppqPPvrIGGNMeXm5+ec//xl8HpOSkozP56vS79tvvz1YY8yYMearr74KbisrKzNr1641EyZMMC1btjS7du2qtG8o54kyxpiZM2ca6Ye7IkydOtUcOnTIGPPDXGWB8xYfH2+++eYbq/qIDEIUrJx6+5baloqTMz722GOVtnm93uB/JBdccIH561//etoQVfG2L1FRUaZFixYmOjo6uF/fvn2rTOQXsH79epOSkhJsG7htTOC2GRVrnCpUIWrHjh3BfaOioqrcRuTU5dSJF405+0OUMT+E1Ouvv77SefZ4PJVu3xIIWac6cuSI6du3b7BNdHS0SUxMdOy2L4EgFliaNWtW421fAkaMGBHcft5555lOnTqZ5OTkShMlnu62L4sXLw6GscD5iIuLC/5cl9u+1MaJEFWXJTBx5SeffBIMSZKM2+0O3r4lJibG/OUvf6nxuajtti8Vn4vmzZubjz/+uNp+l5aWBm+xElji4+Or3BpKUpUJOUMdosrLy819991X6TVc8bZH5513nnnzzTetaiNy+DgPYfXMM8/oL3/5S/BbecePH1fnzp31+OOP69NPP63zt1JmzpypxYsXq2fPniovL1dsbKxSU1P1/PPP66233qpxzp8rr7xSX3zxhWbPnq2+ffuqVatWwXmqunTporvvvluLFy+udFuPUKt4/UZ5ebn27t1b61LdLUACF9tGRUXpv/7rv8LW9/po1aqV3n33Xb3++uu68847lZSUpLKyMh05ckQdOnTQTTfdpNmzZ1d7kXdcXJxWrlyp559/XqmpqYqNjZUxRr1799aSJUuq/YikrqKjozV79mzl5+frnnvuUadOnXT8+HHFxsbqiiuu0IgRIypN3Brwxz/+UVOmTAleNLxz507t2LGjThdOBwwaNEiff/65Ro8erYsuukhlZWWKiYlRamqqpk6dqs8++0yXXXaZ9WMLt+7du+ujjz7SwIED1apVK5WXlyshIUEDBw7Uv//9bw0ZMqROda6++mp98sknuvfee+X1enXixAl16NBBo0aN0ubNmytd71jReeedp5dfflmrVq3SkCFDdOGFF6q8vFyHDh1S69atdf311ys7O1sFBQWnnTLDaS6XSy+99JKWLl2qjIwMJSYm6ujRo0pOTtaoUaO0ceNG3XzzzWHtE86cyxiLqyIBnFVGjhypefPmafDgwVqwYEGkuwMA5wRCFNAIXHjhhfr222+1ZcsWXXjhhZHuDgCcE/g4D2jgduzYoe3bt2vEiBEEKAAII0aiAAAALDASBQAAYIEQBQAAYIEQBQAAYCEm0h2oj/Lycu3evVsJCQkN4o7mAACgYTHGqKSkRO3bt690n8zqNKgQtXv3biUlJUW6GwAAoJHbtWuXOnbsWGubBvVxXkJCQqS7AAAAzgF1yRwNKkTxER4AAAiHumSOsIWojz/+WDfffLMSExMVHx+vHj16aNGiReE6PAAAgKPCck1UXl6e+vXrp9jYWN11113yer1atmyZ7rnnHhUWFurxxx8PRzcAAAAcE/IZy0+cOKFLL71U3377rT788ENdeeWVkqSSkhL993//t7766it98cUX6tKly2lr+f1+eb3eUHYXAABAPp9PHo+n1jYh/zjv/fff17Zt23T33XcHA5T0wwVbTz75pE6cOKGcnJxQdwMAAMBRIQ9ReXl5kqQbb7yxyrbAun/961+h7gYAAICjQh6iCgoKJKnaj+sSExPVqlWrYBsAAICGIuQXlvt8Pkmq8Vomj8ejb7/9ttptZWVlKisrC/7s9/ud7yAAAICFs3qeqBkzZsjr9QYXZisHAABni5CHqMAIVGBE6lS1feNu4sSJ8vl8wWXXrl0h6ycAAEB9hDxEBa6Fqu66p+LiYn3//fc1Tm/gdrvl8XgqLQAAAGeDkIeo9PR0SdI777xTZVtgXaANAABAQxGWyTYvueQSFRUVae3atUpNTZVUebLNzz//XBdffPFpazHZJgAACIe6TLYZ8m/nxcTEaO7cuerXr5969+6trKwseTweLVu2TNu3b9f06dPrFKAAAADOJiEfiQr46KOPNHnyZH344Yc6duyYrrjiCj388MO655576lyDkSgAABAOdRmJCluIcgIhCgAAhMNZce88AACAxogQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYCEm0h0AUHdxcXGO1uvTp49jtW655RbHaj3wwAOO1Vq2bJljtSRp+vTpjtXasGGDY7UAhB8jUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABYIUQAAABZcxhgT6U7Uld/vl9frjXQ3gIj5yU9+4mi9NWvWOFoP9fPggw86VuuPf/yjY7UASD6fTx6Pp9Y2jEQBAABYIEQBAABYIEQBAABYCEuISklJkcvlqnYZM2ZMOLoAAADgqJhwHcjr9erhhx+usj4tLS1cXQAAAHBM2EJU8+bNNWXKlHAdDgAAIKS4JgoAAMBC2EaiysrKNH/+fBUVFSkxMVHXXHONunXrFq7DAwAAOCpsIWrPnj0aNmxYpXX9+/fXggUL1KpVq2r3KSsrU1lZWfBnv98fyi4CAADUWVg+zhs+fLjy8vK0b98++f1+rV27VjfddJPeeust3Xrrrapp0vQZM2bI6/UGl6SkpHB0FwAA4LQidtuX8vJypaenKz8/XytWrNAtt9xSpU11I1EEKZzLuO1L48JtX4Cz11l925eoqCjdd999kmr+Re52u+XxeCotAAAAZ4OIfjsvcC3U4cOHI9kNAACAeotoiFq3bp2kH2Y0BwAAaEhCHqK++OILHTx4sMr6/Px8zZo1S263WwMGDAh1NwAAABwV8ikOlixZouzsbN1www1KSUmR2+3WZ599pnfeeUdRUVGaM2eOOnXqFOpuAAAAOCrkIeq6667Tl19+qfXr1+tf//qXjh49qjZt2mjQoEEaO3asevToEeouAAAAOC7kISo9PV3p6emhPgwAAEBYhW3GcgCN26OPPupYrcsuu8yxWk5fLpCRkeFYrVmzZjlW6/PPP3esVl5enmO1gMaMGxADAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYIEQBAABYcBljTKQ7UVd+v19erzfS3QAipkOHDo7Wu/feex2r9Yc//MGxWqWlpY7V6ty5s2O1JGnVqlWO1XLy+SwoKHCs1rXXXutYLUnau3evo/WAcPD5fPJ4PLW2YSQKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAgssYYyLdibry+/3yer2R7gaAc9igQYMcq/Xyyy87VsvJX+WPPfaYY7Uk6be//a2j9YBw8Pl88ng8tbZhJAoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMBCTKQ7AAANySuvvOJYrZdfftmxWk66+OKLI90FoEFgJAoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMBCvUPUwoULNXr0aKWlpcntdsvlcik3N7fG9n6/X+PGjVNycrLcbreSk5M1btw4+f3+M+k3AABARMXUd4cnnnhCO3bsUKtWrdSuXTvt2LGjxralpaVKT0/Xhg0blJGRoaysLG3cuFHPPfecVq1apfz8fMXHx5/RAwAAAIiEeo9EzZ07V4WFhdq3b5/GjBlTa9vs7Gxt2LBBEyZM0DvvvKNnnnlGK1eu1KRJk7RhwwZlZ2dbdxwAACCS6h2i+vbtq+Tk5NO2M8Zo7ty5atasmSZNmlRp28SJE5WYmKh58+bJGFPfLgAAAERcyC4sLygo0O7du9WzZ88qH9nFxcXp2muvVVFRkb7++utQdQEAACBkQhqiJKlLly7Vbg+sD7SrTllZmfx+f6UFAADgbBCyEOXz+SRJXq+32u0ej6dSu+rMmDFDXq83uCQlJTnfUQAAAAtn9TxREydOlM/nCy67du2KdJcAAAAkWUxxUFeBEaiaRpoCH83VNFIlSW63W2632/nOAQAAnKGQjUSd7pqn010zBQAAcDYLaYhq37691qxZo9LS0krbjh49qtWrV6t9+/bq3LlzqLoAAAAQMiELUS6XSyNHjtShQ4c0bdq0SttmzJih4uJijRw5Ui6XK1RdAAAACJl6XxM1d+5c5efnS5I2b94cXJeXlydJyszMVGZmpiRpwoQJeuONN5Sdna1PP/1U3bt318aNG7Vy5UqlpqZqwoQJzjwKAACAMKt3iMrPz9f8+fMrrVuzZo3WrFkjSUpJSQmGqPj4eOXl5Wnq1KlaunSp8vLy1LZtW40dO1aTJ0/mvnkAAKDBcpkGdN8Vv99f67f5AKAhKS8vd6yWk7/K582b51gtSfrFL37haD0gHHw+X3BOy5qc1fNEAQAAnK1CNk8UAKBh+v777yPdBaBBYCQKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAQkykOwAADcn48eMdq+VyuRyrZYxxrNaiRYscqwU0ZoxEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWCBEAQAAWIiJdAcAIJT69u3raL3p06c7VssY41itF1980bFaW7dudawW0JgxEgUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGCBEAUAAGAhJtIdAIBTZWVlOVZr5syZjtWSpCZNmjhWq7Cw0LFazz33nGO1jh075lgtoDFjJAoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMBCTKQ7AKBxeOSRRxyr9b//+7+O1erQoYNjtSRp586djtXq27evY7W++eYbx2oBqBtGogAAACwQogAAACzUO0QtXLhQo0ePVlpamtxut1wul3Jzc6ttO2XKFLlcrmqXuLi4M+07AABAxNT7mqgnnnhCO3bsUKtWrdSuXTvt2LHjtPsMHTpUKSkplQ8cw+VYAACg4ap3kpk7d666dOmi5ORkPfPMM5o4ceJp9xk2bJj69Olj0z8AAICzUr1DlJPfJgEAAGiowvKZ2gcffKCPPvpI0dHRuvTSS9W3b1+53e5wHBoAACAkwhKiJk2aVOnndu3aaf78+crIyAjH4QEAABwX0ikOUlNTNX/+fBUWFurIkSMqKCjQU089pYMHD+rWW2/Vxo0ba92/rKxMfr+/0gIAAHA2CGmIyszM1L333qvk5GTFxcWpc+fOeuKJJ/T888/r6NGjmj59eq37z5gxQ16vN7gkJSWFsrsAAAB1FpHJNocOHaqYmBitWbOm1nYTJ06Uz+cLLrt27QpTDwEAAGoXkcmaYmNjlZCQoMOHD9fazu12cwE6AAA4K0VkJKqgoEDFxcVVJuAEAABoKEIWokpKSrRp06Yq64uLizVixAhJUlZWVqgODwAAEFJWM5bn5+dLkjZv3hxcl5eXJ+mHi8kzMzO1f/9+devWTWlpaeratatat26toqIirVy5Uvv371dGRobGjh3r3CMBAAAIo3qHqPz8fM2fP7/SujVr1gQvEk9JSVFmZqZatGihBx54QGvXrtXf//53HTx4UPHx8eratasGDx6skSNHKjo62plHAQAAEGb1DlG5ubnKzc09bTuPx6PZs2fb9AkAAOCsF5ELywEAABq6iExxAMBO69atHa23cOFCx2r16dPHsVpOftS/c+dOx2pJUv/+/R2r9c033zhWC0D4MRIFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABgISbSHQAau8zMTMdqTZ482bFakvTjH//YsVrGGMdqvfDCC47VeuyxxxyrJUllZWWO1gPQcDESBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYIEQBQAAYCEm0h0AzkYzZ850rNbYsWMdqxUdHe1YLUnav3+/Y7UeeeQRx2otWLDAsVoAECqMRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFiIiXQHAKekpaU5VuuXv/ylY7Wio6Mdq+W0r776yrFar776qmO1AKAhYCQKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAAiEKAADAgssYYyLdibry+/3yer2R7gbOUrGxsY7VevHFFx2rNXjwYMdqNWnSxLFaTtuzZ49jtWbPnu1YLafNnz/fsVr79u1zrNbx48cdqwVA8vl88ng8tbZhJAoAAMACIQoAAMBCvUJUUVGRfv/73+vGG29Up06dFBsbq7Zt2+qOO+7QunXrqt3H7/dr3LhxSk5OltvtVnJyssaNGye/3+/IAwAAAIiEeoWoF154QWPHjtU333yjjIwMPfLII+rVq5def/11XXPNNVqyZEml9qWlpUpPT9dzzz2nSy65RGPHjtXll1+u5557Tunp6SotLXX0wQAAAIRLTH0a9+jRQ6tXr1bv3r0rrf/ggw90ww036P7779dtt90mt9stScrOztaGDRs0YcIEzZw5M9h+8uTJmjZtmrKzszV16lQHHgYAAEB41WskasCAAVUClCT17t1b1113nQ4cOKDNmzdLkowxmjt3rpo1a6ZJkyZVaj9x4kQlJiZq3rx5akBfDgQAAAhy7MLywFevY2J+GNwqKCjQ7t271bNnT8XHx1dqGxcXp2uvvVZFRUX6+uuvneoCAABA2DgSonbu3Kl3331Xbdu2VdeuXSX9EKIkqUuXLtXuE1gfaAcAANCQ1OuaqOocP35cQ4YMUVlZmbKzsxUdHS3ph0mqJNU4OWZgAqtAu+qUlZWprKws+DPf6AMAAGeLMxqJKi8v1/Dhw7V69WqNGjVKQ4YMcapfkqQZM2bI6/UGl6SkJEfrAwAA2LIOUcYYjRo1SgsXLtTgwYM1Z86cStsDI1A1jTQFRpVqu43LxIkT5fP5gsuuXbtsuwsAAOAoq4/zysvLNXLkSOXk5CgrK0u5ubmKiqqcx053zdPprpmSJLfbHZwuAQAA4GxS75GoigFq0KBBWrBgQfA6qIq6dOmi9u3ba82aNVUm1Tx69KhWr16t9u3bq3Pnzva9BwAAiJB6hajy8nKNGDFCOTk5+vnPf66FCxdWG6AkyeVyaeTIkTp06JCmTZtWaduMGTNUXFyskSNHyuVy2fceAAAgQur1cd60adOUm5urZs2a6eKLL9b06dOrtMnMzFRqaqokacKECXrjjTeUnZ2tTz/9VN27d9fGjRu1cuVKpaamasKECY48CAAAgHCrV4gqLCyUJB06dEhPP/10tW1SUlKCISo+Pl55eXmaOnWqli5dqry8PLVt21Zjx47V5MmTq0zCCQAA0FDUK0Tl5uYqNze3Xgfwer2aNWuWZs2aVa/9AAAAzmaO3fYFAADgXOIyDegOwH6/v9Z5pYCzUVpammO1+vXr51gtSRo4cKBjtS677DLHatX0hZXGZt26dY7V+vzzzx2r9de//tWxWpK0Z88ex2pt2bLFsVpAbXw+X/DuKjVhJAoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMCCyxhjIt2JuvL7/fJ6vZHuBoBq9OrVy7FaTZo0cayW0+677z7HavXs2dOxWikpKY7Vctrx48cdq5Wfn+9YrSVLljhWa/Xq1Y7VkqQtW7Y4Wg/15/P55PF4am3DSBQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFQhQAAIAFlzHGRLoTdeX3++X1eiPdDQBwRMuWLR2rlZCQ4FitH/3oR47VkqSf/vSnjtUaNWqUY7Wc5PP5HK23bt06x2rddNNNjtU6l/h8Pnk8nlrbMBIFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABgwWWMMZHuRF35/X55vd5IdwMAADRyPp9PHo+n1jaMRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFggRAEAAFioV4gqKirS73//e914443q1KmTYmNj1bZtW91xxx1at25dlfZTpkyRy+WqdomLi3PsQQAAAIRbTH0av/DCC5o5c6YuuugiZWRkqHXr1iooKNDy5cu1fPlyvfzyyxo4cGCV/YYOHaqUlJTKB46p16EBAADOKvVKMj169NDq1avVu3fvSus/+OAD3XDDDbr//vt12223ye12V9o+bNgw9enT54w7CwAAcLao18d5AwYMqBKgJKl379667rrrdODAAW3evNmxzgEAAJytHPtMrUmTJj8UrOZjug8++EAfffSRoqOjdemll6pv375VRqsAAAAaEkdC1M6dO/Xuu++qbdu26tq1a5XtkyZNqvRzu3btNH/+fGVkZNRat6ysTGVlZcGf/X6/E90FAAA4Y2c8xcHx48c1ZMgQlZWVKTs7W9HR0cFtqampmj9/vgoLC3XkyBEVFBToqaee0sGDB3Xrrbdq48aNtdaeMWOGvF5vcElKSjrT7gIAADjDnIGTJ0+awYMHG0lm1KhRdd7vT3/6k5Fk7rzzzlrbHT161Ph8vuCya9cuI4mFhYWFhYWFJaSLz+c7bZ6xDlHl5eVm+PDhRpIZPHiwOXnyZJ33LSsrMzExMaZdu3b1OqbP54v4SWVhYWFhYWFp/EtdQpTVx3nl5eUaMWKEXnrpJWVlZSk3N1dRUXUvFRsbq4SEBB0+fNjm8AAAABFX7xBVXl6ukSNHKicnR4MGDdKCBQsqXQdVFwUFBSouLq4yAScAAEBDUa8QFRiBysnJ0c9//nMtXLiwxgBVUlKiTZs2VVlfXFysESNGSJKysrIsugwAABB59ZriYNq0acrNzVWzZs108cUXa/r06VXaZGZmKjU1Vfv371e3bt2Ulpamrl27qnXr1ioqKtLKlSu1f/9+ZWRkaOzYsY49EAAAgHCqV4gqLCyUJB06dEhPP/10tW1SUlKUmpqqFi1a6IEHHtDatWv197//XQcPHlR8fLy6du2qwYMHa+TIkfX+GBAAAOBs4TLGmEh3oq78fr+8Xm+kuwEAABo5n88nj8dTa5sznmwTAADgXESIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsNCgQpQxJtJdAAAA54C6ZI4GFaJKSkoi3QUAAHAOqEvmcJkGNLxTXl6u3bt3KyEhQS6Xq8Z2fr9fSUlJ2rVrlzweTxh7CInzfzbgOYgszn9kcf4jq6Gff2OMSkpK1L59e0VF1T7WFBOmPjkiKipKHTt2rHN7j8fTIJ/AxoLzH3k8B5HF+Y8szn9kNeTz7/V669SuQX2cBwAAcLYgRAEAAFholCHK7XZr8uTJcrvdke7KOYnzH3k8B5HF+Y8szn9knUvnv0FdWA4AAHC2aJQjUQAAAKFGiAIAALBAiAIAALBAiAIAALDQqELUxx9/rJtvvlmJiYmKj49Xjx49tGjRokh365yRkpIil8tV7TJmzJhId69RWLhwoUaPHq20tDS53W65XC7l5ubW2N7v92vcuHFKTk6W2+1WcnKyxo0bJ7/fH75ONzL1eQ6mTJlS43siLi4uvB1vBIqKivT73/9eN954ozp16qTY2Fi1bdtWd9xxh9atW1ftPrwHnFPf838uvP4b1IzltcnLy1O/fv0UGxuru+66S16vV8uWLdM999yjwsJCPf7445Hu4jnB6/Xq4YcfrrI+LS0t/J1phJ544gnt2LFDrVq1Urt27bRjx44a25aWlio9PV0bNmxQRkaGsrKytHHjRj333HNatWqV8vPzFR8fH8beNw71eQ4Chg4dqpSUlErrYmIaza/fsHnhhRc0c+ZMXXTRRcrIyFDr1q1VUFCg5cuXa/ny5Xr55Zc1cODAYHveA86q7/kPaNSvf9MIHD9+3Fx00UXG7Xab9evXB9f7/X5zxRVXmJiYGLN169YI9vDckJycbJKTkyPdjUbtn//8pyksLDTGGDNjxgwjyeTk5FTbdtKkSUaSmTBhQrXrJ02aFOruNkr1eQ4mT55sJJlVq1aFr4ON2GuvvWZWr15dZf3q1atNkyZNTIsWLczRo0eD63kPOKu+5/9ceP03io/z3n//fW3btk133323rrzyyuD6hIQEPfnkkzpx4oRycnIi2EPAGX379lVycvJp2xljNHfuXDVr1kyTJk2qtG3ixIlKTEzUvHnzZJgmrt7q+hzAeQMGDFDv3r2rrO/du7euu+46HThwQJs3b5bEeyAU6nP+zxWNYjwtLy9PknTjjTdW2RZY969//SucXTpnlZWVaf78+SoqKlJiYqKuueYadevWLdLdOucUFBRo9+7d6tevX5WPK+Li4nTttdfq9ddf19dff60uXbpEqJfnjg8++EAfffSRoqOjdemll6pv377nxGzO4dSkSRNJ//cxEe+B8Dr1/FfUmF//jSJEFRQUSFK1b4TExES1atUq2AahtWfPHg0bNqzSuv79+2vBggVq1apVZDp1DqrtPVFxfUFBAf+BhMGpIyHt2rXT/PnzlZGREaEeNS47d+7Uu+++q7Zt26pr166SeA+EU3Xnv6LG/PpvFB/n+Xw+ST9c1Fwdj8cTbIPQGT58uPLy8rRv3z75/X6tXbtWN910k9566y3deuutDJuHUV3eExXbITRSU1M1f/58FRYW6siRIyooKNBTTz2lgwcP6tZbb9XGjRsj3cUG7/jx4xoyZIjKysqUnZ2t6OhoSbwHwqWm8y+dG6//RjEShbPDqX9tXH311VqxYoXS09OVn5+vf/zjH7rlllsi1Dsg/DIzMyv93LlzZz3xxBNq06aNfvGLX2j69Ol69dVXI9O5RqC8vFzDhw/X6tWrNWrUKA0ZMiTSXTqnnO78nwuv/0YxEhX4S6Omvyj8fn+Nf40gtKKionTfffdJktasWRPh3pw76vKeqNgO4TV06FDFxMTwnjgDxhiNGjVKCxcu1ODBgzVnzpxK23kPhNbpzn9tGtPrv1GEqIqfbZ+quLhY33//PZ95R1DgWqjDhw9HuCfnjtreExXX876IjNjYWCUkJPCesFReXq4RI0bopZdeUlZWlnJzcxUVVfm/M94DoVOX81+bxvT6bxQhKj09XZL0zjvvVNkWWBdog/ALzGR76mRrCJ0uXbqoffv2WrNmjUpLSyttO3r0qFavXq327durc+fOEerhua2goEDFxcW8JyyUl5dr5MiRysnJ0aBBg7RgwYJK1+EE8B4Ijbqe/9o0ptd/owhRN9xwgy688EItWrRIGzZsCK4vKSnRU089pZiYmCrfGIOzvvjiCx08eLDK+vz8fM2aNUtut1sDBgwIf8fOUS6XSyNHjtShQ4c0bdq0SttmzJih4uJijRw5Ui6XK0I9bPxKSkq0adOmKuuLi4s1YsQISVJWVla4u9WgBUZAcnJy9POf/1wLFy6s8T9w3gPOq8/5P1de/y7TSL4ytWrVKvXr109ut1tZWVnyeDxatmyZtm/frunTp+vXv/51pLvYqE2ZMkXZ2dm64YYblJKSIrfbrc8++0zvvPOOoqKiNGfOHI0cOTLS3Wzw5s6dq/z8fEnS5s2btX79evXs2TP413RmZmbwYs7S0lL16tUreMuL7t27a+PGjVq5cqVSU1O55YWluj4HhYWFuuCCC5SWlqauXbuqdevWKioq0sqVK7V//35lZGRoxYoVio2NjeTDaVCmTJmiqVOnqlmzZnrooYeqnZMoMzNTqampkngPOK0+5/+cef1HbK70EFi3bp3p37+/8Xq9pmnTpiYtLc0sXLgw0t06J+Tl5ZmBAweazp07m4SEBNOkSRPTsWNHc9ddd5l169ZFunuNxtChQ42kGpfJkydXan/w4EEzduxYk5SUZJo0aWKSkpLM2LFjzcGDByPzABqBuj4HPp/PPPDAA6Z79+6mVatWJiYmxni9XtOrVy8zZ84cc+LEicg+kAbodOde1dyCh/eAc+pz/s+V13+jGYkCAAAIp0ZxTRQAAEC4EaIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAs/H/g3nZfd7IU5gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_num = 10\n",
    "\n",
    "# Random select 3 examples from imgDisp and testset\n",
    "np.random.seed(1)\n",
    "idx = np.random.choice(len(imgDisp), sample_num)\n",
    "\n",
    "# Select instances\n",
    "imgDisp_select = [imgDisp[i] for i in idx]\n",
    "testset_select = [testset[i] for i in idx]\n",
    "\n",
    "# Check the selected instances' labels are the same\n",
    "for i in range(sample_num):\n",
    "    x = testset_select[i][0].view(-1, input_size)\n",
    "\n",
    "    # Using model to predict character\n",
    "    y_pred_list = []\n",
    "    for j in range(len(mdl_logic_list)):\n",
    "        y_pred_list.append(mdl_logic_list[j].predict(x))\n",
    "\n",
    "    y_pred = np.argmax(np.array(y_pred_list), axis=0)[0]\n",
    "\n",
    "    # Display image from imgDisp\n",
    "    fig, ax = plt.subplots(figsize=(7,7))\n",
    "    ax.imshow(imgDisp_select[i][0], cmap='gray')\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "    ax.set_title(f\"Label: {imgDisp_select[i][1]}, Prediction Label: {y_pred}\", fontsize=label_size)\n",
    "\n",
    "    print(f'Sample {i+1}: imgDisp label is {imgDisp_select[i][1]}, testset label is {testset_select[i][1]}, predict label is {y_pred}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66044bfc-06d7-4575-a5eb-5ccf95d9e71b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 1.90 seconds\n",
      "Accuracy: 0.8778\n"
     ]
    }
   ],
   "source": [
    "# Define logic regression classifier\n",
    "mdl_logic_ovo = OneVsOneClassifier(LogisticRegression(max_iter=1000))\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_logic_ovo.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Make predictions and evaluate the model\n",
    "y_pred = mdl_logic_ovo.predict(x_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84d5696a-73d1-40a2-8607-69205a883d86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training class 0 (145) vs class 1 (154), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 3 (149), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 5 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 6 (146), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 8 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 0 (145) vs class 9 (140), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 3 (149), Training time: 0.00 seconds, Precision: 0.9655, Recall (Sensitivity): 1.0000, Specificity: 0.9706, Accuracy: 0.9839, F1-Score: 0.9825\n",
      "Training class 1 (154) vs class 4 (135), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 5 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 7 (145), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 8 (144), Training time: 0.03 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 1 (154) vs class 9 (140), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 1 (154), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 3 (149), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 5 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 8 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 2 (144) vs class 9 (140), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 1 (154), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9706, Specificity: 1.0000, Accuracy: 0.9839, F1-Score: 0.9851\n",
      "Training class 3 (149) vs class 2 (144), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 5 (135), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9706, Specificity: 1.0000, Accuracy: 0.9877, F1-Score: 0.9851\n",
      "Training class 3 (149) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 8 (144), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 3 (149) vs class 9 (140), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9706, Specificity: 1.0000, Accuracy: 0.9865, F1-Score: 0.9851\n",
      "Training class 4 (135) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 1 (154), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 3 (149), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 5 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 6 (146), Training time: 0.00 seconds, Precision: 0.9787, Recall (Sensitivity): 1.0000, Specificity: 0.9714, Accuracy: 0.9877, F1-Score: 0.9892\n",
      "Training class 4 (135) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 8 (144), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 4 (135) vs class 9 (140), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 5 (135) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 5 (135) vs class 1 (154), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 5 (135) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 5 (135) vs class 3 (149), Training time: 0.00 seconds, Precision: 0.9792, Recall (Sensitivity): 1.0000, Specificity: 0.9706, Accuracy: 0.9877, F1-Score: 0.9895\n",
      "Training class 5 (135) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 5 (135) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9787, Specificity: 1.0000, Accuracy: 0.9878, F1-Score: 0.9892\n",
      "Training class 5 (135) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 5 (135) vs class 8 (144), Training time: 0.00 seconds, Precision: 0.9787, Recall (Sensitivity): 0.9787, Specificity: 0.9667, Accuracy: 0.9740, F1-Score: 0.9787\n",
      "Training class 5 (135) vs class 9 (140), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 1 (154), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 3 (149), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9714, Specificity: 1.0000, Accuracy: 0.9877, F1-Score: 0.9855\n",
      "Training class 6 (146) vs class 5 (135), Training time: 0.00 seconds, Precision: 0.9722, Recall (Sensitivity): 1.0000, Specificity: 0.9787, Accuracy: 0.9878, F1-Score: 0.9859\n",
      "Training class 6 (146) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 8 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 6 (146) vs class 9 (140), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 1 (154), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 2 (144), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 3 (149), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 5 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 8 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 7 (145) vs class 9 (140), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9706, Specificity: 1.0000, Accuracy: 0.9865, F1-Score: 0.9851\n",
      "Training class 8 (144) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 1 (154), Training time: 0.02 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 3 (149), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 4 (135), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 5 (135), Training time: 0.00 seconds, Precision: 0.9667, Recall (Sensitivity): 0.9667, Specificity: 0.9787, Accuracy: 0.9740, F1-Score: 0.9667\n",
      "Training class 8 (144) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 7 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 8 (144) vs class 9 (140), Training time: 0.01 seconds, Precision: 0.9667, Recall (Sensitivity): 0.9667, Specificity: 0.9750, Accuracy: 0.9714, F1-Score: 0.9667\n",
      "Training class 9 (140) vs class 0 (145), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 9 (140) vs class 1 (154), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 9 (140) vs class 2 (144), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 9 (140) vs class 3 (149), Training time: 0.01 seconds, Precision: 0.9756, Recall (Sensitivity): 1.0000, Specificity: 0.9706, Accuracy: 0.9865, F1-Score: 0.9877\n",
      "Training class 9 (140) vs class 4 (135), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 9 (140) vs class 5 (135), Training time: 0.01 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 9 (140) vs class 6 (146), Training time: 0.00 seconds, Precision: 1.0000, Recall (Sensitivity): 1.0000, Specificity: 1.0000, Accuracy: 1.0000, F1-Score: 1.0000\n",
      "Training class 9 (140) vs class 7 (145), Training time: 0.00 seconds, Precision: 0.9756, Recall (Sensitivity): 1.0000, Specificity: 0.9706, Accuracy: 0.9865, F1-Score: 0.9877\n",
      "Training class 9 (140) vs class 8 (144), Training time: 0.01 seconds, Precision: 0.9750, Recall (Sensitivity): 0.9750, Specificity: 0.9667, Accuracy: 0.9714, F1-Score: 0.9750\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 定义计算分类统计数据的函数\n",
    "def cls_counts(y_true, y_proba):\n",
    "    tp = np.sum((y_true == 1) & (y_proba[:, 1] >= 0.5))\n",
    "    fp = np.sum((y_true == 0) & (y_proba[:, 1] >= 0.5))\n",
    "    tn = np.sum((y_true == 0) & (y_proba[:, 1] < 0.5))\n",
    "    fn = np.sum((y_true == 1) & (y_proba[:, 1] < 0.5))\n",
    "    return y_proba, (tp, fp, tn, fn)\n",
    "\n",
    "# 定义计算性能指标的函数\n",
    "def get_scores(tp, fp, tn, fn):\n",
    "    precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "    recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "    specificity = tn / (tn + fp) if (tn + fp) > 0 else 0\n",
    "    accuracy = (tp + tn) / (tp + fp + tn + fn) if (tp + fp + tn + fn) > 0 else 0\n",
    "    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n",
    "    return precision, recall, specificity, accuracy, f1\n",
    "\n",
    "# 加载数据集\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 获取类别列表\n",
    "class_list = np.sort(np.unique(y_train))\n",
    "\n",
    "# 创建模型矩阵以保存模型\n",
    "mdl_logic_matrix = {}\n",
    "for cls_p in class_list:\n",
    "    mdl_logic_matrix[cls_p] = {}\n",
    "    for cls_n in class_list:\n",
    "        if cls_p == cls_n:\n",
    "            continue\n",
    "        mdl_logic_matrix[cls_p][cls_n] = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# 训练和测试模型\n",
    "for cls_p in class_list:\n",
    "    # 正类训练数据\n",
    "    x_train_ovo_p = X_train[(y_train == cls_p), :]\n",
    "    y_train_ovo_p = np.ones(x_train_ovo_p.shape[0])\n",
    "\n",
    "    # 正类测试数据\n",
    "    x_test_ovo_p = X_test[(y_test == cls_p), :]\n",
    "    y_test_ovo_p = np.ones(x_test_ovo_p.shape[0])\n",
    "\n",
    "    for cls_n in class_list:\n",
    "        if cls_p == cls_n:\n",
    "            continue\n",
    "\n",
    "        # 负类训练数据\n",
    "        x_train_ovo_n = X_train[(y_train == cls_n), :]\n",
    "        y_train_ovo_n = np.zeros(x_train_ovo_n.shape[0])\n",
    "\n",
    "        # 负类测试数据\n",
    "        x_test_ovo_n = X_test[(y_test == cls_n), :]\n",
    "        y_test_ovo_n = np.zeros(x_test_ovo_n.shape[0])\n",
    "\n",
    "        # 合并训练数据\n",
    "        x_train_ovo = np.concatenate((x_train_ovo_p, x_train_ovo_n), axis=0)\n",
    "        y_train_ovo = np.concatenate((y_train_ovo_p, y_train_ovo_n), axis=0)\n",
    "\n",
    "        # 模型训练\n",
    "        start_time = time.time()\n",
    "        mdl_logic_matrix[cls_p][cls_n].fit(x_train_ovo, y_train_ovo)\n",
    "        end_time = time.time()\n",
    "\n",
    "        # 合并测试数据\n",
    "        x_test_ovo = np.concatenate((x_test_ovo_p, x_test_ovo_n), axis=0)\n",
    "        y_test_ovo = np.concatenate((y_test_ovo_p, y_test_ovo_n), axis=0)\n",
    "\n",
    "        # 测试模型\n",
    "        y_proba_ovo = mdl_logic_matrix[cls_p][cls_n].predict_proba(x_test_ovo)[:, 1]  # 输出概率\n",
    "\n",
    "        # 显示结果\n",
    "        y_proba_ovo, (tp, fp, tn, fn) = cls_counts(y_test_ovo, np.column_stack((1-y_proba_ovo, y_proba_ovo)))\n",
    "        precision, recall, specificity, accuracy, f1 = get_scores(tp, fp, tn, fn)\n",
    "        print(f'Training class {cls_p} ({x_train_ovo_p.shape[0]}) vs class {cls_n} ({x_train_ovo_n.shape[0]}), Training time: {end_time - start_time:.2f} seconds, Precision: {precision:.4f}, Recall (Sensitivity): {recall:.4f}, Specificity: {specificity:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b38a8ec2-0b0a-4d33-b0d8-d59aa8abc06d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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